scholarly journals Multiscale Segmentation of Microscopic Images

2020 ◽  
Author(s):  
Dimiter Prodanov
Keyword(s):  
Microscopic Images ◽  
Multiscale Segmentation

The study of needle type influence on woven fabric surface area during the sewing process

2021 ◽  
pp. 004051752110191
Author(s):  
Beti Rogina-Car ◽  
Stana Kovačević
Keyword(s):  
Computer Program ◽  
Tensile Properties ◽  
Surface Area ◽  
Cotton Fabrics ◽  
Woven Fabric ◽  
Microscopic Images ◽  
Needle Type ◽  
Fabric Surface ◽  
Needle Point

The aim of this study was to investigate the damage to cotton fabrics (ticking and damask) caused by stitching with three types of needle point shapes (R, SES and SUK) and four needle sizes (70, 80, 90 and 100 Nm). Damage to the yarn and the surface area of the hole were investigated. Based on the results, it can be concluded that two types of damage occur during sewing: the needle passes through the warp/weft (it displaces the yarn) and the needle damages the warp/weft. An analysis and comparison of the surface area of the holes was carried out, obtained by a computer program based on microscopic images. The results show greater damage to the yarn at the needle piercing point in the ticking due to higher density, friction and low yarn migration. The largest surface area of the holes was produced when sewing with SUK-designated needles on ticking and damask. When sewing damask, R-designated needles cause the least damage to the piercing point, whereas SES-designated needles give the best results when sewing the ticking. Thread damage was further confirmed by testing the tensile properties of the yarn at the needle piercing points.

A robust technique based on invariant moments – ANFIS for recognition of human parasite eggs in microscopic images

2008 ◽  
Vol 35 (3) ◽  
pp. 728-738 ◽  
Author(s):  
Esin Dogantekin ◽  
Mustafa Yilmaz ◽  
Akif Dogantekin ◽  
Engin Avci ◽  
Abdulkadir Sengur
Keyword(s):  
Microscopic Images ◽  
Invariant Moments ◽  
Human Parasite

Skin Melanoma Detection in Microscopic Images Using HMM-Based Asymmetric Analysis and Expectation Maximization

2021 ◽  
pp. 1-1
Author(s):  
Rozita Rastghalam ◽  
Habibollah Danyali ◽  
Mohammad Sadegh Helfroush ◽  
M. Emre Celebi ◽  
Mojgan Mokhtari
Keyword(s):  
Expectation Maximization ◽  
Skin Melanoma ◽  
Microscopic Images ◽  
Melanoma Detection

scholarly journals Deep learning approach to describe and classify fungi microscopic images

PLoS ONE ◽  
2020 ◽  
Vol 15 (6) ◽  
pp. e0234806 ◽  
Author(s):  
Bartosz Zieliński ◽  
Agnieszka Sroka-Oleksiak ◽  
Dawid Rymarczyk ◽  
Adam Piekarczyk ◽  
Monika Brzychczy-Włoch
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Microscopic Images

scholarly journals Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Fetulhak Abdurahman ◽  
Kinde Anlay Fante ◽  
Mohammed Aliy
Keyword(s):  
Object Detection ◽  
Malaria Parasite ◽  
Blood Smear ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Detection Accuracy ◽  
Small Object ◽  
Thick Blood Smear ◽  
Malaria Parasites ◽  
Microscopic Images

Abstract Background Manual microscopic examination of Leishman/Giemsa stained thin and thick blood smear is still the “gold standard” for malaria diagnosis. One of the drawbacks of this method is that its accuracy, consistency, and diagnosis speed depend on microscopists’ diagnostic and technical skills. It is difficult to get highly skilled microscopists in remote areas of developing countries. To alleviate this problem, in this paper, we propose to investigate state-of-the-art one-stage and two-stage object detection algorithms for automated malaria parasite screening from microscopic image of thick blood slides. Results YOLOV3 and YOLOV4 models, which are state-of-the-art object detectors in accuracy and speed, are not optimized for detecting small objects such as malaria parasites in microscopic images. We modify these models by increasing feature scale and adding more detection layers to enhance their capability of detecting small objects without notably decreasing detection speed. We propose one modified YOLOV4 model, called YOLOV4-MOD and two modified models of YOLOV3, which are called YOLOV3-MOD1 and YOLOV3-MOD2. Besides, new anchor box sizes are generated using K-means clustering algorithm to exploit the potential of these models in small object detection. The performance of the modified YOLOV3 and YOLOV4 models were evaluated on a publicly available malaria dataset. These models have achieved state-of-the-art accuracy by exceeding performance of their original versions, Faster R-CNN, and SSD in terms of mean average precision (mAP), recall, precision, F1 score, and average IOU. YOLOV4-MOD has achieved the best detection accuracy among all the other models with a mAP of 96.32%. YOLOV3-MOD2 and YOLOV3-MOD1 have achieved mAP of 96.14% and 95.46%, respectively. Conclusions The experimental results of this study demonstrate that performance of modified YOLOV3 and YOLOV4 models are highly promising for detecting malaria parasites from images captured by a smartphone camera over the microscope eyepiece. The proposed system is suitable for deployment in low-resource setting areas.

scholarly journals Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3068
Author(s):  
Soumaya Dghim ◽  
Carlos M. Travieso-González ◽  
Radim Burget
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
The Other ◽  
Support Vector ◽  
Learning Approaches ◽  
Microscopic Images ◽  
Trained Neural Network ◽  
Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

Innovative parameters obtained for digital analysis of microscopic images to evaluate in vitro hemorheological action of anesthetics

2015 ◽  
Author(s):  
Analía I. Alet ◽  
Sabrina Basso ◽  
Marcela Delannoy ◽  
Nicolás A. Alet ◽  
Mabel D'Arrigo ◽  
...  
Keyword(s):  
Digital Analysis ◽  
Microscopic Images

Wave optical effects in Lorentz microscopic images of magnetic domain walls

1983 ◽  
Vol 36 (1-2) ◽  
pp. 98-110 ◽  
Author(s):  
Sonoko Tsukahara ◽  
Hisazo Kawakatsu
Keyword(s):  
Domain Walls ◽  
Magnetic Domain ◽  
Microscopic Images ◽  
Optical Effects ◽  
Magnetic Domain Walls

An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images

2013 ◽  
Vol 24 (7-8) ◽  
pp. 1887-1904 ◽  
Author(s):  
Subrajeet Mohapatra ◽  
Dipti Patra ◽  
Sanghamitra Satpathy
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Early Diagnosis ◽  
Lymphoblastic Leukemia ◽  
Ensemble Classifier ◽  
Microscopic Images ◽  
Classifier System

New computational solution to quantify synthetic material porosity from optical microscopic images

2010 ◽  
Vol 240 (1) ◽  
pp. 50-59 ◽  
Author(s):  
V.H.C. DE ALBUQUERQUE ◽  
P.P. REBOUÇAS FILHO ◽  
T.S. CAVALCANTE ◽  
J.M.R.S. TAVARES
Keyword(s):  
Synthetic Material ◽  
Microscopic Images ◽  
Material Porosity ◽  
Computational Solution
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